Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures

@article{CastroCamilo2020BayesianSG,
  title={Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures},
  author={Daniela Castro-Camilo and Linda Mhalla and Thomas Opitz},
  journal={Extremes},
  year={2020},
  volume={24},
  pages={105-128}
}
We develop a method for probabilistic prediction of extreme value hot-spots in a spatio-temporal framework, tailored to big datasets containing important gaps. In this setting, direct calculation of summaries from data, such as the minimum over a space-time domain, is not possible. To obtain predictive distributions for such cluster summaries, we propose a two-step approach. We first model marginal distributions with a focus on accurate modeling of the right tail and then, after transforming… Expand

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